Measurement of the jet mass in high transverse momentum Z(-> b(b)over-bar)gamma production at root s=13 TeV using the ATLAS detector

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Contents lists available atScienceDirect

Physics

Letters

B

www.elsevier.com/locate/physletb

Measurement

of

the

jet

mass

in

high

transverse

momentum

Z

(

→

bb

)

γ

production

at

√

s

=

13 TeV using

the

ATLAS

detector

.The ATLASCollaboration

a rt i c l e i n f o a b s t r a c t

Articlehistory:

Received17July2019

Receivedinrevisedform27November2020 Accepted30November2020

Availableonline3December2020 Editor: M.Doser

The integrated ﬁducial cross-section and unfolded differential jet mass spectrum of high transverse momentum Z→bb decays are measuredin Zγ events inproton–proton collisions at√s=13 TeV.

The data analysed were collected between 2015 and 2016 with the ATLAS detector at the Large

Hadron Collider and correspond to an integrated luminosity of 36.1 fb−1. Photons are required to have a transverse momentum pT>175 GeV. The Z→bb decay is reconstructed using a jet with

pT>200 GeV, foundwith the anti-kt R=1.0 jet algorithm,and groomed to removesoftand

wide-angleradiationand tomitigatecontributionsfromthe underlyingevent andadditional proton–proton collisions.Twodifferentbutrelatedmeasurementsareperformedusingtwojetgroomingdefinitionsfor reconstructingthe Z→bb decay:trimmingandsoftdrop.Thesealgorithmsdifferintheirexperimental and phenomenological implications regarding jet mass reconstruction and theoretical precision. To identifyZ bosons,b-taggedR₌0.2 track-jetsmatchedtothegroomedlarge-R calorimeterjetareusedas aproxyfortheb-quarks.Thesignalyieldisdeterminedfromfitsofthedata-drivenbackgroundtemplates tothedifferentjetmassdistributionsforthetwogroomingmethods.Integratedfiducialcross-sections andunfolded jetmassspectraforeachgroomingmethodare comparedwithleading-ordertheoretical predictions.TheresultsarefoundtobeingoodagreementwithStandardModelexpectationswithinthe currentstatisticalandsystematicuncertainties.

Contents

1. Introduction . . . 2

2. ATLASdetector . . . 2

3. DataandMonteCarlosimulation . . . 2

4. Eventreconstructionandselection . . . 3

5. Signalandbackgroundestimation . . . 4

6. Deﬁnitionoftheobservableandcorrectionfordetectoreffects . . . 5

7. Systematicuncertainties . . . 5

8. Results . . . 7

8.1. Fitresultsandsigniﬁcanceestimate . . . 7

8.2. Integratedﬁducialcross-sectionmeasurement . . . 7

8.3. Differentialﬁducialcross-sectionmeasurement . . . 7

9. Conclusion . . . 7

Declarationofcompetinginterest . . . 9

Acknowledgements . . . 9

References . . . 9

TheATLASCollaboration . . . 11

_E-mail_address:_atlas_{.publications}_@cern_.ch_. https://doi.org/10.1016/j.physletb.2020.135991

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1. Introduction

ThisLetter presentsa measurement ofthefiducial and differ-ential jet mass cross-sectionsofhightransverse momentum (pT) Z bosonsthatdecayintobb pairs¯ andareproducedinassociation witha photon, denotedby Z(→bb¯)γ.The analysisuses proton– proton(pp)collisiondatacollectedin2015and2016bytheATLAS detector [1] attheLargeHadronCollider(LHC)atacenter-of-mass energyof√s=13 TeV.Thismeasurementoftheunfoldedjetmass spectrum of hadronically decaying Z bosonsat theLHC explores the experimental features and phenomenological implications of techniquesusedtoreconstructboostedbosons –coloursinglets – decayingintobb.¯ Similarmeasurementsofgluons –colouroctets – decayingintobb pairs¯ havealsobeenmadebytheATLAS Collab-oration [2].The Z(→bb¯)γ processprovidesawell-defined exper-imental signature formeasuring massiveboosted Z bosonsusing high-pT jetscontaining pairsofb-quarks.A detailed study ofthe Z→bb signal¯ isimportant forassessingsystematicuncertainties andidentification techniquesfor themeasurement of H→bb in¯ the high-pT range, as well asfor potential TeV-scale resonances decaying intodibosons,one ofthem beinga Z boson ora Higgs bosondecayingintobb [¯ 3,4].

The Z(→bb¯)γ channeloffersadvantagesinaccessingthe Z→ bb signal¯ compared to the inclusive channels studied in Run 1 by ATLAS [5] and in Run 2 by CMS [6] since it provides both a useful trigger signature via the photon and an opportunity to directly estimate background processes usingthe data. Initial re-sultsofthemodellingofjetkinematicsinthe Z(→bb¯)γ channel using 13 TeV data collected by ATLAS are presented in Ref. [7]. The measurement described in this Letter selects bb decays¯ of a Z boson contained within a single jet, referred to as a Z -jet, with transversemomentum pZ -jet_T >200 GeV anda photon with transverse momentum pγ_T >175 GeV. The high-pT requirement enhancesthe signal overthe dominant γ +jets background pro-duction, which has a softer pT spectrum. The candidate Z -jet is reconstructed using a ‘groomed’ anti-kt [8] jet with radius

pa-rameter R=1.0 (large-R jet). A multivariate algorithm is used to determinewhether R=0.2 track-jetsthat are associatedwith the large-R jet are b-tagged, i.e. ifthey contain b-hadron decay products.TheapproachtotaggingpresentedinthisLetterisbuilt uponafoundationofstudiesfromLHCrunsat√s=7 and8 TeV, includingextensivestudiesofjetreconstructionandgrooming al-gorithms [9–11] and detailed investigations of track-jet-based b-tagginginboostedtopologies [7,12].

Twodifferentjetgroomingalgorithmsareusedtoperformthe measurement: ‘trimming’ [10], and‘soft drop’ [11,13].The exper-imental and phenomenological implications for jet mass recon-structionandtheoreticalprecisionaredifferentforthetwo groom-ingalgorithms. Thetrimmingalgorithmisthedefaultusedin AT-LAS to study boosted bosons, chosen as a result ofoptimisation studiesperformedfromLHCrunsat√s=8 and13 TeV [14].The soft-dropcalculationsachieveadifferenttheoreticalprecisionand offer advantages such as the formal absence of non-global loga-rithms. The distribution ofthe soft-drop massfor QCD processes hasnowbeencalculatedbothatnext-to-leadingorder(NLO)with next-to-leading-logarithm (NLL) accuracy [15,16] and at leading order (LO) with next-to-next-to-leading-logarithm (NNLL) accu-racy [17,18].Thislevel ofprecision forajet substructure observ-able at a hadron collider is surpassed only by the calculation of thrust ine+e− interactions [19].Similar calculationsarenot cur-rentlyavailablefortrimmedjets.

Thedoubledifferentialcross-sectionofsoft-dropjetsasa func-tionofthemassandtransversemomentumwerepreviously mea-suredbyATLAS [20] andCMS [21] inbalanceddijeteventsat√s= 13 TeV.The trimmedjetmassdistributionindijetandW/Z +jets eventswas measuredbyCMSat√s=7 TeV [22].Whileprevious

analyses measured thecross-section of quark andgluon-initiated jetsfordifferentgroomingalgorithms,thisanalyses measuresthe massoflarge-R jetscontaining thehadronicdecayproductsof Z bosonsinZ(→bb¯)γ eventsat√s=13 TeV.

2. ATLASdetector

The ATLAS detectorat theLHC is a multipurposeparticle de-tector with a forward–backward symmetric cylindrical geometry and a near 4π coverage in solid angle.1 It consists of an inner detector (ID) for tracking surrounded by a thin superconducting solenoidprovidinga2 Taxialmagneticﬁeld,electromagneticand hadroniccalorimeters,andamuonspectrometer.TheIDcoversthe pseudorapidity range|η| <2.5. It consistsofsilicon pixel,silicon microstrip,andtransitionradiationtrackingdetectors.A newinner pixel layer, the insertable B-layer [23,24], was added at a mean radius of 3.3 cm during the period between Run 1 and Run 2 oftheLHC.Lead/liquid-argon(LAr)samplingcalorimetersprovide electromagnetic(EM)energymeasurements withhighgranularity (|η| <3.2). The hadronic calorimeter uses a steel/scintillator-tile samplingdetectorin the centralpseudorapidity range (|η| <1.7) anda copper/LArdetector inthe region 1.5 <|η| <3.2. The for-ward regions (3.2 <|η| <4.9) are instrumented withcopper/LAr andtungsten/LAr calorimetermodules optimised for electromag-netic and hadronic measurements, respectively. A muon spec-trometer with an air-core toroid magnet system surrounds the calorimeters.Threelayersofhigh-precisiontrackingchambers pro-videcoverageintherange|η| <2.7,whilededicatedfastchambers allow triggering in the region |η| <2.4. The ATLAS trigger sys-temconsistsofahardware-based ﬁrst-leveltriggerfollowedby a software-basedhigh-leveltrigger [25].

3. DataandMonteCarlosimulation

Thedatawere collectedin pp collisionsattheLHCwith√s= 13 TeV and a 25 nsproton bunch crossing interval during 2015 and2016. The full data sample corresponds to an integrated lu-minosityof 36.1 fb−1 afterrequiringthat alldetectorsubsystems were operational during data recording. The uncertainty in the combined2015–2016integratedluminosity is2.1% [26], obtained usingtheLUCID-2 detector[27] for theprimary luminosity mea-surements.Collisioneventswererecordedwithatriggerselecting eventswithatleastone photoncandidatewithtransverseenergy ET>140 GeV.

MonteCarlo(MC) eventsamplesthat includeanATLAS detec-torsimulation [28] based on Geant 4 [29] areusedtomodelthe Zγ signal and the small tt¯+γ and Wγ background contribu-tions.Inaddition, γ+jets MCeventsamplesareusedtostudythe triggermodelling. Inadditionto thehard scatter,each eventwas overlaid with additional pp collisions (pile-up) according to the distribution ofthe average number of pp interactions per bunch crossing,μ,observedindata.Theseadditionalpp collisionswere generatedwith Pythia 8.1 [30] using theATLAS A2set of tuned parameters [31] andtheNNPDF23LO [32] partondistribution func-tion(PDF)set.Simulatedeventswerethenreconstructedwiththe samealgorithmsasthoserunoncollisiondata.

1 _ATLAS_uses_a_right-handed_coordinate_system_with_its_origin_at _the_nominal interactionpointinthecentreofthedetector.Thepositivex-axisisdeﬁnedbythe directionfromtheinteractionpointtothecentreoftheLHCring,withthepositive

y-axispointingupwards,whilethebeamdirectiondeﬁnesthez-axis.Cylindrical coordinates(r,φ)areusedinthetransverse plane,φ beingtheazimuthal angle aroundthez-axis.Thepseudorapidityηisdeﬁnedintermsofthepolarangleθ

byη= −ln tan(θ/2).Rapidityisdeﬁnedasy=0.5ln[(E+pz)/(E−pz)]whereE

denotestheenergyand pzisthecomponentofthemomentumalongthebeam

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The Zγ signal was modelled using the LO Sherpa 2.1.1 [33] generator, withthe CT10NLO [34] PDFset;thesample isﬂavour inclusive ( Z(→qq)γ). An alternative Zγ sample was produced with MadGraph 5.2 [35],whichgeneratedLOmatrixelementsthat werethenpartonshoweredwith Pythia 8.1usingtheNNPDF23LO PDF set andtheATLAS A14 setof tuned parameters [36] forthe underlying event.This alternativesignal sample is usedto deter-mine the systematicuncertaintyassociated withthe signal mod-elling.

The γ+jets sampleswerealsogeneratedwith Sherpa 2.1.1and theCT10NLOPDFset.Thematrixelementwasconfiguredtoallow aphotonwithuptothreepartonsinthefinalstate.Thett¯+γ pro-cesses were modelled by MadGraph 5.2 interfacedto Pythia 8.1. NLO corrections were applied to the tt¯+γ cross-section [37]. The Wγ MCsampleswithhadronicallydecaying W bosonswere generated using Sherpa 2.1.1,witha configurationsimilar tothat usedforthe Zγ sample.PredictionsforWγ productionwere nor-malisedaccordingtothecross-sectionsprovidedbythegenerator.

4. Eventreconstructionandselection

Eventsarerequiredtohaveareconstructedprimaryvertex, de-ﬁned as the vertex with at least two reconstructed tracks with pT>0.4 GeV andwiththehighestsumofsquaredtransverse mo-mentaofassociatedtracks [38].

Hadronicallydecayinghigh-pT Z→bb candidates¯ areidentiﬁed usinglarge-R jetstocapturebothb-quarks,sincetheywillbevery closeduetothehighLorentzboost.Thetwo differentjet groom-ingalgorithmsconsideredintheanalysis,trimming andsoftdrop, differintheirpile-upmitigationandmassresolutionperformance. Trimmed calorimeter jets Trimmed calorimeter jets are recon-structed from noise-suppressedtopological clusters(topoclusters) of calorimeter energy deposits calibrated to the local hadronic scale (LC) [39], using the anti-kt algorithm with radius

parame-ter R=1.0 implemented in FastJet [40,41].Trimmedcalorimeter jetsarethosejetstowhichthetrimmingalgorithm [10] isapplied. The aim of this algorithm is to improve the jet mass resolu-tion and its stability with respect to pile-up by discarding the softer components of jets that originate from initial-state radia-tion,pile-upinteractions,ortheunderlyingevent.Thisisdoneby reclusteringtheconstituentsoftheinitiallarge-R jet,usingthekt

algorithm [42,43], into subjets with radius parameter Rsub=0.2 andremoving anysubjetthat hasa pT lessthan5% ( fcut) ofthe parentjet pT.Thejetmassmjet,themainobservableinthis anal-ysis, is deﬁnedas themagnitude ofthe four-momentum sumof constituentsinsideajet.Itisreferred toasthecalorimeter-based mass ifit is calculated using the topoclustersasconstituents, or as the track-assisted jet mass [44] if it is estimated by using tracking information.Thejet massfortrimmed jetsisdeﬁnedas theweighted combinationofthecalorimeter-based massandthe track-assistedjetmass [44],whereeachinputmassisweightedby afactorproportionaltotheirinverse-squaredmassresolution. Soft-drop calorimeter jets Soft-drop calorimeter jets are formed by the applicationof the soft-drop algorithm [11] to the anti-kt

R=1.0 jets described above, with additional topological cluster preprocessing that isdescribed below.The soft-drop algorithm is designed to remove soft andwide-angle radiation and also con-tamination from pile-up. In the ﬁrst step of the grooming algo-rithm,theanti-kt R=1.0 jetsarereclusteredwiththeCambridge–

Aachen (C/A) [45,46] algorithm sothat theconstituents are com-bined purely accordingto their angularseparation. Thesoft-drop algorithm then reverses the C/A algorithm clustering historyand removes thesofter subjetata speciﬁc step of theC/A clustering historyunlessthesoft-dropconditionisfulﬁlled:

min(pT1,pT2) pT1+pT2 >zcut R12 R0 β ,

where zcut andβ are algorithm parameters, pT1 and pT2 arethe transversemomentaofthedeclusteredsubjetsateachhistorystep, R12 isthedistancebetweenthesubjetsinthe(η, φ) spaceand R0 isathresholdcorresponding tothejetradius.The parameters

β=0 and zcut=0.1 areusedin theanalysis,based onthe stud-iesinRef. [47]. Thefinal measurementis performedforjetmass mjet>30 GeV,whichimpliesthatanycollineardivergenceis reg-ulated andthe measurement remains protected against collinear singularities.Thesoft-dropjetmassexhibitsapile-updependence withthechosenparametersandthereforeaspecialversionof pile-up suppressed topological clustersare used to construct the jets that are then groomed withthe soft-drop algorithm. Specifically, theSoftKiller(SK)algorithm [48] isusedinconjunctionwith Con-stituent Subtraction (CS) [49,50] based on the studies presented inRef. [47]. CS is applied before the SK algorithm.The CS is an extension of the pile-up subtraction based on jet area [51]. The algorithmproceedsasfollows.First,virtualparticleswith infinites-imally small pT (ghosts) are addedto theevent(eachcovering a fixed area in the η–φ plane) with energy density matching the medianenergydensityoftheevent.Second,theaddedghostsare matchedto the topologicalclustersin η–φ spaceand onlythose within R=0.25 ofthetopoclusterarefurtherconsideredforthe pile-upremovalprocedure.Thealgorithmproceedstheniteratively througheach topocluster–ghost pairinorder ofascending R.If thepT ofthetopoclusterislargerthanthatofthematchedghost, thepT oftheindividualtopoclusteriscorrectedbysubtractingthe pT of theghost andtheghost are removed. Otherwisethe pT of the topocluster is subtracted from the pT of the ghost and the pT ofthetopoclusterissettozero.TheSKalgorithmexploitsthe characteristicthat particlesoriginatingfrompile-upcollisions are softerthan those from thehard-scattering collision andremoves particlesthatfallbelowacertain pT threshold,determinedonan event-by-event basis. The pile-up suppressed topological clusters afterCSandSKareusedasinputtothesoft-drop jet reconstruc-tion.Thecalorimeter-basedjetmassisusedforsoft-dropjets. Allgroomedjets A dedicatedMC-based calibration,similar tothe procedure used inRef. [44], is applied to correctthe jet pT and massofboth thetrimmedjetsandthesoft-drop jetsto the par-ticle level. To account for semileptonic decays of the b-hadrons, thefour-momentumofthe closestreconstructed muoncandidate within R=0.2 oftheb-taggedtrack-jetistakenintoaccountin thecalorimeter-based componentofthejet massobservable (see belowforthedescriptionofthetrack-jetdefinitionandb-tagging). MuoncandidatesareidentifiedbymatchingIDtrackstofulltracks ortracksegmentsreconstructedinthemuonspectrometer.Muons are required to have pT>10 GeV and |η| <2.4, and to satisfy theloose identification criteriaofRef. [52], whichimpose quality requirements on the tracks,but no isolation criteria are applied. A calibration is applied to correct the muon transverse momen-tum,andreconstruction andidentificationefficiencyscalefactors, derivedfrom J/ψ→μ+μ− and Z→μ+μ− events [52], are ap-pliedtosimulation.Large-R jetsarerequiredtohavepT>200 GeV and|η| <2.0.A comparisonofthecalibrated Z -jet mass distribu-tion andthe particle-leveljet mass distribution fortrimmedjets andsoft-dropjetsisshowninFig.1.Particle-leveljets,usedinthe unfoldingprocedure described in Section 6,are builtfrom stable final-stateparticles (definedasthose withproper lifetime τ cor-respondingto cτ >10 mm) excluding muonsand neutrinos and using the same jet reconstruction algorithms used for calorime-terjets. Similarly to themuon-in-jet correction at reconstruction leveldescribedinSection4,particle-levelmuonsareaddedtothe particle-leveljet if they are within R=0.2 ofa b-hadron. The

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Fig. 1. Comparisonof(a)thecalibratedreconstructedZ -jet massdistributionand(b)theparticle-leveljetmassdistributionofsoft-drop(dashedline)andtrimmedjets(solid line)inthesignalregionintheZγ sample.

mass ofparticle-leveljetsisdefinedastheinvariant massof the four-vector sum of its constituents. The jet mass distribution of soft-drop jets is significantly broader than that of trimmed jets for both the reconstructed jet mass, Fig. 1(a), and the particle-leveljetmass,Fig.1(b),whereasthedistributionfortrimmedjets is more asymmetric than forsoft-drop jets atparticle-level. This asymmetryisafeatureofthetrimmingalgorithmandis indepen-dentofthequarkflavourfromthe Z bosondecay.Thejetmassis very stable with respect to pile-up for both jet definitions [53]. The soft-drop jets exhibit basically no pile-up dependence due to the constituent-level pile-upsuppression techniqueswhile the trimmedjetmassvariesby0.14 GeV perreconstructedvertex [53]. WhileRef. [53] focussesonthehadronicdecayofW bosons,itwas found that the sameconclusions holdaswell for jetscontaining heavyflavourdecaysof Z bosons.

Track-jets Small-radius jets formed from charged-particle tracks are usedasprobes ofb-hadronsassociatedwithlarge-R jetsthat may containthe candidate Z→bb jets.¯ Track-jets are builtwith the anti-kt algorithm with a radius parameter of R=0.2 [12]

fromatleasttwoID trackswithpT>0.5 GeV and |η| <2.5 [54]. Only track-jets with pT>10 GeV and |η| <2.5 are used and they are associated with the large-R calorimeter jets via ghost-association [51,55],inwhichthetrack-jetsare includedinthejet clusteringprocedure withinfinitesimally small pT such that they havenoeffectonthejetclusteringresult.Track-jetscontaining b-hadron decay products are tagged with a multivariate algorithm known as MV2c10, which exploits the presence of large-impact-parameter tracks, the topological decay chain reconstruction and thecorrespondingdisplacedverticesfromb-hadrondecays [56,57]. The MV2c10 algorithm is configured to achieve an efficiency of 70% fortaggingb-jets in a MCsample of tt events,¯ while reject-ing80%ofc-jetsandmorethan99%oflight(quarkorgluon)jets in the same sample. Thisconfiguration is referred to as the70% workingpoint (WP).For MC samples,thetagging efficiencies are correctedtomatchthosemeasuredindata [54,58,59].These small-radius track-jets are referred to as b-jets. By using this small-R definition, b-jets can be reliably identified inthe dense environ-ment ofboosted bosons.Consequently, the numberof associated b-jets (Nb-jet) provides anessential criterion fortheidentification

ofmerged Z→bb decays.¯

Photons Photoncandidatesarereconstructedfromclustersof en-ergy deposits in the EM calorimeter [60]. The photon energy is calibratedbyapplyingtheenergyscalesmeasuredwithZ→e+e− decays [61]. Identiﬁcationrequirementsareapplied toreduce the

contamination from π0 _or _other _neutral _hadrons _decaying _into photons.RequirementsontheshowershapeintheEMcalorimeter andontheenergyfractionmeasured inthe hadroniccalorimeter areusedto identifyphotons.Photons mustsatisfythe tight iden-tificationandisolationcriteriadefinedinRef. [60],andmusthave |η| <1.37 or 1.52 <|η| <2.37.For MC samples, the photon re-construction,identificationandisolation efficienciesare corrected tomatch thosemeasured in data [60,61]. The selectedphoton is requiredtohave pγ_T >175 GeV,which isdeterminedby an opti-misation ofthe expected signal significance,2 andto ensure that the trigger is fully efficient. The efficiency of the photon selec-tionrangesbetween95%and98%forphotonswithpγ_T >175 GeV depending on the pseudorapidity of the photon. These selection criteriaareinvertedtoformasample ofnon-tight photonsforthe backgroundestimatedescribedinSection5.

Qualityrequirementsareappliedtophotoncandidatesto iden-tify those arising from instrumental problems or non-collision background [62], and events containing such candidates are re-jected. In addition, quality requirements are applied to remove eventscontainingspurious jetsfromdetectornoiseorout-of-time energydepositsinthecalorimeterfromcosmicraysorother non-collisionsources [63].

Selected events are required to have at least one groomed large-R jetand at leastone photon, with R(jet, γ) >1.0 from thegroomedlarge-R jetaxis.Thegroomed large-R jetisrequired tohavep_TZ -jet>200 GeV tocapturebothofthedecayproductsof the Z→bb decay,¯ i.e.bothjetsfromtheb-quarksshouldbefully containedinthegroomedlarge-R jet.Inthesignalregion,thejets identiﬁed ascandidate Z →bb decays¯ mustcontain atleast two ghost-associatedtrack-jets,andthetwo withthehighestpT must betaggedasb-jets(Nb-jet=2).

5. Signalandbackgroundestimation

Toextractthe Z(→bb¯)γ signalfromthedata,signaland back-groundtemplatesobtainedfromMCsimulationandfromdataare fittedtotheobserved Z→bb candidate¯ jet massdistribution us-ing a binned maximum-likelihood fit.This procedure is repeated separatelyforeachofthegroomedjetdefinitionsusedtoperform themeasurement.Thedominantbackgroundis γ+jets withgluon tobb splitting.¯ Lesssignificantbackgroundcontributionsaredueto tt¯+γ andWγ processes.Otherbackgroundssuchasmultijetand

2 _The_expected_signal_{signiﬁcance}_is_deﬁned_as_s_/√_s₊_b,_where_{s is}_the_number ofexpectedsignaleventsandb isthenumberofexpectedbackgroundevents.

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Table 1

Deﬁnitionsofthecontrolregions(CR)andthesignalregion(SR)usedforthe data-drivenbackgroundestimateoftheγ+jets process.

Nb-jet=0 Nb-jet=1 Nb-jet=2

Non-tightγ CR-A CR-C CR-E

Tightγ CR-B CR-D SR

W/Z +jetprocesses,whereajet ismisidentiﬁed asa photon,and theassociatedproductionofaHiggsbosonwitha γ arefoundto benegligible(<1%).

TemplatesofthejetmassdistributionfortheZ(→bb¯)γ signal, andforthet¯t+γ andWγ backgrounds,aredeterminedfromMC simulation. In contrast,the template used to estimate the domi-nant background contribution from γ +jets processes isderived directly fromthe measured data withoutinput from MC simula-tion. It is especially important to minimise the reliance on MC simulationsforthisprocess,asMCgeneratorshavenotbeentested thoroughlyinthehighpTregionofthebb production¯ phasespace for γ+jets.

The data-drivenbackgroundestimate ofthe jetmass distribu-tion for the γ+jets process relies on two features of the ﬁnal state:the b-jetmultiplicity (i.e. Nb-jet)andthe photon

identiﬁca-tioncriteria(i.e.tightvsnon-tight).Theb-jetmultiplicity require-ment isused toisolate the γ+jets process, which dominatesin samples with Nb-jet=0 or 1. Furthermore,the ratio of γ+jets

yields(Nγ+jets₎_in_events_with_tight_compared_with_non-tight pho-tonsisobservedtobeapproximatelyindependentof Nb-jet.These

two characteristicsareusedto modeltheexpected γ +jets yield inthesignalregionviaatransfer-factor(TF)method.Thismethod extrapolatesthesignalregion(SR)yieldfromcontrolregions(CRs)

with Nb-jet=1 and the shapeof thejet mass distributionin the

signalregionforthe γ+jets backgroundfromCRswithNb-jet=2

but non-tight photons. The deﬁnitions of the different CRs are summarised in Table 1. In these CRs, the tt¯+γ and Wγ con-tributions aresubtractedfromthedata,asthemassshapediffers fromthatof γ+jets.

The γ +jets background estimates areconstructed in 10 GeV binsof Z→bb candidate¯ jetmass.A binsizeof10 GeVischosen basedonthelarge-R jetmassresolution.Foreachjet massbin,i, in each CR, the estimated yield of γ +jets events in that bin is calculatedas:

N_CRγ+_,_ijets=NCR,i−N_CRtt¯+_,γ_i −NW_CRγ_,_i

whereNt_CR¯t+_,_iγ andN_CRW_,γ_i arethenumberoft¯t+γ andWγ events, respectively, taken directly fromthe MC simulation. The system-atic uncertaintiesfort¯t+γ andWγ contributions aredescribed inSection7.Thecontributionfromsignaleventsineachofthese control regionsisnegligiblecomparedto otherprocessesandhas noimpactonthebackgroundestimation.

Toobtaintheestimateofthenumberof γ+jets eventspresent ineachbinofthejetmassdistributionintheSR(Nγ_SR+_,_ijets),thejet massdistributionfromCR-E(N_CRγ+₋jets_E_,_i)ismultipliedbyaTF deter-minedfromthe Nb-jet=1 regions:CR-CandCR-D.Thisprocedure

maybesummarisedas N_SRγ+_,_ijets= ⎛ ⎝N γ+jets CR−D,i Nγ_CR+₋jets_C_,_i ⎞ ⎠Nγ_CR+₋jets_E_,_i,

where the ratio N_CRγ+₋jets_D_,_i/N_CRγ+₋jets_C_,_i isthe TF in each bin of thejet massdistribution.The valueoftheTFvarieswithjetmass, rang-ing from 1.2forjet massesof 30 GeV to 0.8at 160 GeV, andis within5% ofunityfrom50to110 GeV. WiththisTFmethod,the

shape of the jet mass distribution in the signal region is deter-minedfromCR-E (with Nb-jet=2) andthenormalisation ofeach

binisdeterminedfromtheNb-jet=1 controlregions.

The validity of this approach relies on the assumption that the TF does not depend on Nb-jet. This is tested in data, using

the Nb-jet=0 sampleasa cross-check,andinMC simulation

us-ing Nb-jet=0, 1, and 2. The differences in the TFs between the

Nb-jet=0 and Nb-jet=1 controlregions indataare takenas

sys-tematicuncertainties intheTFs,asdescribed inSection 7.The TF approachwas further validatedby comparing the TF,determined

fromthe Nb-jet=1 regions, indataandMC.Withintheir

statisti-calprecision,theTFwerefoundtobecompatible.Additionally,the reconstructedjetmassdistributionofthedata-drivenbackground estimateintheSRisingoodagreementwiththejetmass distri-butionin γ+jets eventssimulatedwith Sherpa 2.1.1.

6. Deﬁnitionoftheobservableandcorrectionfordetectoreffects

Thereconstructedjetmassdistributionsfromthesignalregions arecorrected toparticlelevelin ordertomeasure thefull differ-entialcross-sectionoftheZ→bb jet¯ mass.Unfoldingaccountsfor the effects of detectorresolution and ineﬃciency and allows di-rectcomparisonswithparticle-levelpredictions.Theparticle-level eventselection issimilar to the selection described inSection 4. Eventsarerequiredtohaveatleastoneparticle-leveljetwithpT> 200 GeV,|η| <2.0 andtwo ghost-associatedb-hadrons. Theyare alsorequiredto havea particle-levelphoton with pT>175 GeV, |η| <1.37 or1.52 <|η| <2.37 and R(jet, γ) >1.0.

Theestimatedbackgroundjetmassspectrumissubtractedfrom the data in the signal region, as discussed in Section 8. The background-subtracteddistributionofthereconstructedjetmassis thenunfoldedusinganiterativeBayesiantechnique [64] withone iteration.Thistechnique isimplemented intheRooUnfold frame-work [65]. Oneiteration ischosen tominimisethe statistical un-certaintiesaswellastheuncertaintiesassociatedwiththe unfold-ingmethodestimatedwithadata-drivenclosuretestasdescribed inSection 7.The unfoldingprocedure corrects forbinmigrations betweentheparticle-levelandthereconstructedjetmass distribu-tionusingaresponsematrixthat describestheprobability foran eventwithaparticle-leveljetmassinbini tobereconstructedin bin j.Theresponsematrixisconstructedfromeventsthatsatisfy theeventselectionandﬁducialregioncriteriaatboththeparticle levelandthereconstructionlevel.Theparticle-leveljetsand recon-structed jetsare required tobe matched within R=0.75. Fur-thermore,theunfoldingprocedurecorrectsforeventsthat satisfy eithertheparticle-levelorreconstructedselectioncriteria,butnot both.Theresponsematrixisobtainedfromthe Sherpa Z(→bb¯)γ signalMCsimulation.The Z→bb candidate¯ jetmassdistribution was rebinned in the highjet mass region to improve the corre-lationbetweenthe reconstructedandparticle-level jet mass.The unfoldingresultsarefoundtobe compatiblewhenincreasingthe numberofiterationsused,attheexpenseofanincreaseinthe sta-tisticaluncertainties.Theunfoldingprocedureisalsovalidatedby unfoldingthe jetmass spectrausinga singular value decomposi-tion(SVD)technique [66] andwithinthestatisticalprecisionofthe measurements,theresultsarefoundtobecompatible.

7. Systematicuncertainties

Varioussources ofsystematicuncertaintiesimpactthe Z→bb¯ candidate jet mass distribution. These are classiﬁed into experi-mentalandtheoretical uncertainties, and uncertainties relatedto thebackgroundestimateandtheunfoldingprocedure.The system-aticuncertaintiescanhaveanimpactontheshapeofthejetmass distribution andon thesignal andbackground yields. Systematic

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Table 2

Theuncertaintiesintheintegratedﬁducialcross-sectionmeasurementfromdatain thesignalregionfortrimmedandsoft-dropjets.Multipleindependentcomponents arecombinedintogroupsofsystematicuncertainties.

Source Uncertainty [%]

Trimmed jets Soft-drop jets

Luminosity 2.1 2.1

Jet energy resolution 0.4 <0.1 Jet mass resolution 5.1 6.0 Jet energy and mass scale 7.2 7.4

b-tagging 5.3 5.8

Photon related 1.3 1.2 Muon related 0.1 <0.1 Photon trigger 0.4 0.4 Transfer factor: 0-tag vs 1-tag 7.5 4.0 Transfer factor: statistical 2.9 1.5

t¯t+γrelated 1.7 2.8

Wγrelated <0.1 <0.1

Zγ modelling 12 15

Unfolding non-closure 9.4 5.8 Signal MC response: statistical 3.9 6.0 Background template: statistical 5.9 13 Fit statistical uncertainty 30 39

Total uncertainty 37 46

uncertainties areevaluatedbyvarying eachsourcebyplusor mi-nus one standard deviationofits uncertainty. The ﬁt isrepeated foreachvariationandthejet massdistributionunfoldedto parti-clelevel. Thejet energyandmass scaleuncertainties are treated ascorrelatedwhileallothersourcesofsystematicuncertaintiesare treatedasuncorrelated.

The impact of the systematic uncertainties on the integrated ﬁducial cross-section measurements, grouped by source, is sum-marisedinTable2.

Forgroomed large-R jets, the uncertainties inthe energyand massscalesareestimatedbyusingthedouble-ratiotechnique de-scribed in Ref. [44] by comparing the calorimeter jet properties withthemeasurementsofthesamejetreconstructedfromtracks intheID.Theuncertaintiesinthejetmassandenergyresolutions areassessedbyapplyingadditionalsmearingofthejetobservables accordingtotheuncertaintyintheirresolutionmeasurements.An absolute uncertainty of 2% is used for the jet energy resolution whilea relativeuncertainty of20%isused forthe jetmass reso-lution,consistent withprevious studies ofboththe trimmedand soft-dropjetdeﬁnitions [20,67].

The b-tagginguncertaintyisevaluated byvarying the data-to-MC correctionsin various kinematic regions, based on the mea-suredtaggingeﬃciencyandmistagrates.Thesevariationsare ap-pliedseparatelytob-hadronjets,c-hadronjets,andlightjets, lead-ing to three uncorrelated systematic uncertainties. An additional uncertaintyisincludedtoaccountfortheextrapolationtojetswith pT beyondthekinematicreachofthedatacalibration [54,58,59].

The impact of the systematicuncertainties on thephoton re-construction, identificationandisolation efficienciesis studiedby varying the scale factors, used to correct the respective efficien-cies in simulation to matchthose observed in data, within their uncertainties.Theuncertaintiesaredeterminedfromdatasamples of Z→ +−γ (with =e, μ), Z→e+e−, andinclusivephoton events,usingthe methodsdescribed inRef. [60].Uncertaintiesin the photon energy scale and resolution are also taken into ac-count [61].

The uncertainties associated withthe muon momentum cali-bration and resolution, and the reconstruction and identiﬁcation eﬃciencyscalefactors,arederivedfromZ→μ+μ−events [52].

Theuncertaintyassociatedwiththemodellingofpile-upinthe simulationisassessedbyvaryingthereweightingofthepile-upin thesimulationwithinitsuncertainties.Thisuncertaintycoversthe

differencebetweentheratiosofpredictedandmeasuredinelastic cross-sectionvalues [68].

The eﬃciencyof thephoton triggeris 100% forphotons with Eγ_T >175 GeV, with an uncertainty of 0.5% that is propagated throughtheunfolding.

The systematic uncertainties associated with the data-driven backgroundtemplateareestimatedbyderivingthebin-by-bin nor-malisationfromCR-AandCR-BwithNb-jet=0 insteadoffromthe

Nb-jet=1 CRs as described in Section 5 (referred to as ‘Transfer

factor:0-tagvs1-tag’).Anadditionaluncertaintyinthebin-by-bin normalisationofthebackgroundtemplateisderivedbyvaryingthe jet mass distributions inCR-C andCR-D (with Nb-jet=1) within

theirstatisticaluncertainty(referredtoas‘Transferfactor: statisti-cal’).

Thesignalandbackgroundyieldsare estimatedby performing asimultaneousﬁttothedata.Theuncertaintyinthenormalisation ofthebackgroundtemplate,arisingfromthestatisticaluncertainty inthe data, isreferred to asthe ﬁtstatistical uncertainty inthe following.

Themodellinguncertainties affectingthe Wγ processare de-rivedbycomparingthenominal Sherpa 2.1.1samplewithone pro-ducedusingthe MadGraph [35] generator interfacedto Pythia 8. For the t¯t+γ background, three different sources of modelling uncertainties are considered: the uncertainty due to the parton showerandhadronisation isestimatedby comparingthenominal samples produced using MadGraph interfaced to Pythia 8, with MadGraphinterfacedto Herwig 7 [69,70];theuncertaintydueto differentinitial- andﬁnal-stateradiationconditionsisestimatedby using Pythia 8tuned parameterswithhighorlow QCDradiation activity;andtheuncertaintiesduetothechoiceofrenormalisation andfactorisationscalesareestimatedbyusingalternativesamples withthescalesvariedindependentlybyfactorsof2and0.5.

For the Zγ process, the modelling uncertainty is derived by replacingthenominalsamplewiththealternative MadGraph sam-ple,interfacedto Pythia 8.Thefitisrepeatedwiththealternative MC signal sample and then unfolded using the response matrix, signalefficiencyandfakefractionfromthisalternativesignal sam-ple.Uncertaintiesinthesignal efficiencyandresponsematrixare alreadycoveredbyexperimentalsystematicerrorsoutlinedearlier. The systematicuncertaintydue tothe dependence ofthe un-folding on the prior signal distribution, as obtained from MC simulations,is evaluated througha data-driven ‘closure test’.The simulatedsignal sample isreweighted atparticle levelsuch that the distribution of the fully simulated reconstructed jet mass more closely matches the observed data. Pseudo-data from the reweightedsignalMCsamplearethenunfoldedusingtheresponse matrix fromthe original unweighted signal MC sample, and the unfoldedresultiscomparedwiththereweightedparticle-level dis-tribution. Differences observed in this comparison are taken as systematicuncertainties in the unfolding, and are referred to as unfolding non-closure uncertainties in the following. The uncer-taintyduetothedependenceonthenumberofunfoldingiteration steps is negligible. The statisticaluncertainties in the signal MC sample, usedto build theresponse matrix,andbackground tem-platesarealsoconsidered.

Abootstrappingprocedure [71] isusedtoensurethatthe sys-tematicuncertainties arestatisticallysigniﬁcant.Foreach system-atic uncertainty considered, pseudo-experiments are constructed fromthedataorMC simulationbyassigningeacheventaweight takenfroma Poisson distribution withunit mean. The statistical uncertaintyinthesystematicvariationistakenastheRMSacross thepseudo-experiments.Thejet massdistributionforeachofthe systematicvariationsisthenrebinneduntilatargetsigniﬁcanceof 1.5 standarddeviationsisachieved.

Thedominantsystematicuncertainties on theintegrated ﬁdu-cialcross-sectionmeasurementsarisefromtheuncertaintiesinthe

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Table 3

Thenumberofdataeventsobservedinthe signal re-gion,alongwiththe compositionoftheseeventsafter theﬁtinthe30<mZ -jet_<_{160 GeV mass}_range.

Num-bers arepresented for trimmedand soft-dropjets at reconstructionlevel.Statisticalandsystematic uncertain-tiesare addedin quadrature.Systematic uncertainties aredescribedinSection7.

Process Trimmed jets Soft-drop jets

Zγ 215±61 167±73 γ+jets 4180±90 4630±100 Wγ 39±8 37±8 tt¯+γ 39±12 40±12 Total 4480±110 4870±120 Data 4475 4874

ﬁt,thesignal modelling,thedata-drivenbackgroundestimate,the jet mass andenergyscales, andthe jet massresolution.The un-certaintyinthepile-upmodellinginMCsimulationisfoundtobe negligible.

As shown in Table 2 the impact of the statistical uncertain-ties on thefiducial cross-section of the response matrixand the background template is significantly different for trimmed and soft-drops jets. This behaviour can be explained by the differ-encesofthesignalandbackgroundjetmassdistributionsbetween trimmedandsoft-drop jetsandtheir interplay withthe smooth-ing of those uncertainty components.For trimmedjets, only the variations inthecoreofthejetmassdistributionsarestatistically significantwhileforsoft-dropjets,thesignaldistributionis signif-icantly widerandthus thetails alsocontribute to thesystematic uncertainty. Furthermore,the backgroundjetmass distributionof soft-drop jets is not as steeply falling as for trimmed jets. This results in less susceptibilityto statistical fluctuationsaround the signal jet mass peak which would otherwise be reduced by the rebinningprocedureintroducedabove.

8. Results

Results of the measurement of the jet mass distribution in Z(→bb¯)γ eventsarereportedinthefollowingthreesubsections; fitresultsandthecalculationofthesignificanceofthesignalabove the background,the unfolded fiducial cross-section measurement usingthefull measuredjet massspectrum,andthe unfolded dif-ferentialspectrumofthejetmassitself.

8.1. Fitresultsandsigniﬁcanceestimate

Thesignalyieldisextractedbysimultaneouslyﬁttingthesignal and the backgroundtemplates described in Section 5 to the ob-served Z→bb candidate¯ jet mass(mZ -jet₎ _{distribution.} _{A binned}

maximum-likelihood fitis performedinthe mass rangebetween 30 and 160 GeV using a bin widthof 10 GeV. The upper mass bound is chosen to exclude the mass region near the top quark masswhilethelowermassboundischosentoexcludetheregion of jet mass for which the uncertainty in the calibration is large andto protect againstcollinear singularities,asdiscussedin Sec-tion4.Theresultofthefittothereconstructed Z→bb candidate¯ jet massdistribution isshowninFigs. 2(a)and 2(b) fortrimmed and soft-drop jets along with their corresponding background-subtracteddatadistributionsinFigs.2(c)and 2(d).Thefittedsignal yieldis215±61 eventsfortrimmedjetsand167±73 eventswhen usingsoft-dropjetsasshowninTable3.

The13bins ofthe Z→bb candidate¯ jetmassdistributionare combinedinaprofilelikelihoodfit [72] toextracttheexpectedand observedsignificances.Systematicuncertaintiesareincludedinthe

Table 4

Expectedand observedsigniﬁcancevalues(innumbersof stan-darddeviations)fortrimmedandsoft-dropjetsforamassrange between30and160 GeV.

Trimmed jets Soft-drop jets Expected signiﬁcance 3.8 2.7 Observed signiﬁcance 3.9 2.7

fit asnuisance parameters andare assumedto be Gaussian dis-tributed.The expectedandobservedsignificancesoftheStandard Model prediction fitted to the observed data for the Z(→bb¯)γ production are summarised in Table 4. For each jet definition, the observedsignificance is consistent withthe expectation. Dif-ferences in the significance between the two jet definitions are relatedtothedifferencesinboththe jetmassresolutionandthe fiducialcross-sectionsbetweentrimmedandsoft-dropjets,which affectthe signal andbackgroundyields inthe 30–160 GeVmass window.

8.2.Integratedﬁducialcross-sectionmeasurement

Themeasured integratedfiducial cross-sectionsinthe boosted (high pT) Z →bb region¯ are listed in Table 5. The integrated fiducial cross-section is extractedfrom the ratio of the unfolded yield of signal events and the total integrated luminosity. The measurements are compared with the Sherpa 2.1.1 and Mad-Graph+Pythia 8 LO predictions described in Section 3. Mad-Graph+Pythia 8predictsaround 30% fewereventsthanthe sam-ples generated with Sherpa. Withinthe current uncertainties on the measurement,both predictions are consistent with the mea-sured cross-sections for soft-drop jets. For trimmed jets, larger differencescanbeobservedbetweenthe MadGraph+Pythia 8 pre-diction and the measured cross-section. The uncertainties in the measuredintegratedfiducialcross-sectionresultsaresummarised inTable 2.The dominant sourceof systematicuncertainty isthe fituncertaintyforbothjetdefinitions.Theuncertaintyinthe nor-malisationofthe backgroundtemplatehasa largeimpact onthe cross-sectionmeasurementbecauseoftheorderofmagnitude dif-ferencebetweentheestimatednumbersofsignalandbackground events.

8.3.Differentialﬁducialcross-sectionmeasurement

Thedifferential fiducialcross-section of Z(→bb¯)γ production asafunctionofthe Z→bb jet¯ mass,obtainedfromtheunfolded datainthesignal region,isshowninFig.3fortrimmedand soft-dropjets.Asacomparison,thepredictionfrom Sherpa 2.1.1atLO isalso shown. Statistical uncertainties are significant for the dif-ferentialfiducialcross-sectionmeasurement inthetailsofthejet massdistribution.

9. Conclusion

The fully unfolded differential jet mass spectrum for the high-pT Z→bb signal¯ usingthe Zγ final state andthe fiducial productioncross-sectionaremeasuredin36.1 fb−1ofpp collisions at√s=13 TeV recordedin2015and2016bytheATLASdetector. Thehigh-pT Z→bb signal¯ isreconstructedusinglarge-R jetsand jetsubstructuretechniques,includingdoublesubjetb-tagging.Two differentgroomingalgorithmsare usedinthisanalysis:trimming andsoftdrop.Thesegroomingalgorithmsexhibitdifferencesinthe measuredshapesofthe jet massspectraandtheresulting preci-sionof those measurements. The soft dropjet mass spectrum is observedtobebroaderandmoresymmetricthanthatoftrimmed jets.Theprecisionofthefiducialcross-sectionmeasuredisslightly

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Fig. 2. Thereconstructedjetmassdistributioninthesignalregion(a,b)afterfittingthe Sherpa 2.1.1signalmodelandbackgroundtemplatestothedataand(c,d)the correspondingbackground-subtracteddistributionsfor(a,c)trimmedand(b,d)soft-dropjets.Theratioofdatatothefittedsignalplusbackgroundisshownatthebottom inFigures(a)and(b).Thebackground-subtractedjetmassdistributionsindataarecomparedwiththereconstructedsignaljetmassdistributionsinthenominalMonte Carlosimulation.Theerrorbarsonthebackground-subtracteddatadistributionarestatisticalonly.The Sherpa signalmodelandbackgroundtemplatearescaledtofitthe dataasdescribedinSection5.

Table 5

Measuredandpredictedcross-sectionforZ(→bb¯)γinthepZ -jetT >200 GeV,30<mZ -jet<160 GeV andpγT>175 GeV region.

Jet deﬁnition σ(Z(→bb¯)γ, pTZ -jet>200 GeV, p γ

T>175 GeV, 30<mZ -jet<160 GeV) [fb] Trimmed jets Data 17.0±5.0 (stat.)±3.6 (syst.)

SherpaZγ prediction 13.4±0.2 (stat.) MadGraph+Pythia 8 Zγ prediction 9.1±0.1 (stat.)

Soft-drop jets Data 12.5±4.9 (stat.)±3.1 (syst.) SherpaZγ prediction 15.4±0.1 (stat.)

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Fig. 3. Unfoldeddistributionofthe Z→bb candidate¯ jetmassfrom background-subtracteddatainthesignalregionalongwiththepredictionsfrom Sherpa 2.1.1. Resultsfor(a)trimmedand(b)soft-dropjetsareshown.Theerrorbarscorrespond tothestatisticaluncertaintywhilethehatchedbandcorrespondstothetotal sys-tematicuncertaintyinthemeasurement.Thestatisticaluncertaintyinthe Sherpa signalisnegligibleandnotshownintheﬁgure.

better for trimmed jets. Within the fiducial regions defined for eachjetdefinitionasp_TZ -jet>200 GeV,30 <mZ -jet_<_{160 GeV and}

pγ_T >175 GeV,theproductioncross-sectionsaremeasuredtobe:

σtrimming

Z(→bb¯)γ =17.0±5.0 (stat.)±3.6 (syst.) fb,

σsoft drop

Z(→bb¯)γ =12.5±4.9 (stat.)±3.1 (syst.) fb.

Theintegratedﬁducialcross-sectionmeasurementsandthe differ-ential cross-section ofthejet massof theboosted Z→bb decay¯ arefoundtobeinagreementwiththeLOpredictionsfrom Sherpa and MadGraph+Pythia 8(integrated), andfrom Sherpa (differen-tial),respectively,withinthecurrentstatisticalandsystematic un-certainties.Futuremeasurements ofresonant bb decays¯ willneed toaccount fortheobserved experimentalfeaturesofthese differ-entgrooming algorithmsinconcert withtheavailabilityof preci-siontheoreticalcalculationsofthejetmassspectra,orlackthereof.

Declarationofcompetinginterest

Theauthorsdeclarethattheyhavenoknowncompeting ﬁnan-cialinterestsorpersonalrelationshipsthatcouldhaveappearedto inﬂuencetheworkreportedinthispaper.

Acknowledgements

We thankCERN for thevery successful operation of theLHC, aswell asthe support stafffrom ourinstitutions without whom ATLAScouldnotbeoperatedeﬃciently.

WeacknowledgethesupportofANPCyT,Argentina;YerPhI, Ar-menia;ARC,Australia;BMWFW andFWF,Austria;ANAS, Azerbai-jan;SSTC,Belarus;CNPqandFAPESP,Brazil;NSERC,NRC andCFI, Canada;CERN;ANID,Chile;CAS,MOSTandNSFC,China; COLCIEN-CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Repub-lic; DNRF and DNSRC, Denmark; IN2P3-CNRSand CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRT, Greece;RGCandHongKongSAR,China;ISFandBenoziyoCenter, Israel;INFN, Italy;MEXTandJSPS,Japan;CNRST, Morocco;NWO, Netherlands;RCN, Norway;MNiSW andNCN, Poland;FCT, Portu-gal; MNE/IFA,Romania; JINR;MES ofRussia andNRCKI, Russian Federation;MESTD,Serbia;MSSR,Slovakia;ARRSandMIZŠ, Slove-nia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation,Sweden;SERI,SNSF andCantonsofBernandGeneva, Switzerland;MOST,Taiwan; TAEK,Turkey;STFC,UnitedKingdom; DOE and NSF, United States of America. In addition, individual groupsandmembershavereceived supportfromBCKDF,Canarie, ComputeCanada, CRCandIVADO, Canada; BeijingMunicipal Sci-ence& Technology Commission,China;COST, ERC, ERDF,Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; In-vestissements d’Avenir Labex, Investissements d’Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-ﬁnanced by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; La Caixa Bank-ing Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; GöranGustafssonsStiftelse,Sweden;TheRoyalSocietyand Lever-hulmeTrust,UnitedKingdom.

The crucialcomputing support fromall WLCG partners is ac-knowledged gratefully,inparticular fromCERN, theATLAS Tier-1 facilitiesat TRIUMF(Canada),NDGF(Denmark, Norway, Sweden), CC-IN2P3 (France),KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1(Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) andBNL (USA),theTier-2facilitiesworldwideandlargenon-WLCGresource providers.Majorcontributorsofcomputingresources arelistedin Ref. [73].

References

[1]ATLASCollaboration,TheATLASexperimentattheCERNLargeHadronCollider, J.Instrum.3(2008)S08003.

[2]ATLASCollaboration,Propertiesofg→bb at¯ smallopeninganglesinpp

colli-sionswiththeATLASdetectorat√s=13 TeV,Phys.Rev.D99(2019)052004, arXiv:1812.09283 [hep-ex].

[3]ATLASCollaboration,SearchforheavyresonancesdecayingintoaW orZ

bo-sonandaHiggsbosoninﬁnalstateswithleptonsandb-jetsin36 fb−1 of

√

s=13 TeV pp collisionswiththeATLASdetector,J.HighEnergyPhys.03 (2018)174,arXiv:1712.06518 [hep-ex].

[4]ATLASCollaboration,Searchforheavyresonancesdecayingtoaphotonanda hadronicallydecayingZ/W/H bosoninpp collisionsat√s=13 TeV withthe ATLASdetector,Phys.Rev.D98(2018)032015,arXiv:1805.01908 [hep-ex]. [5]ATLASCollaboration,Measurementofthecrosssectionofhightransverse

mo-mentum Z→bb production¯ inproton–protoncollisionsat√s=8 TeV with theATLASdetector,Phys.Lett.B738(2014)25,arXiv:1404.7042 [hep-ex]. [6]CMSCollaboration,InclusivesearchforahighlyboostedHiggsbosondecaying

toabottomquark-antiquarkpair,Phys.Rev.Lett.120(2018)071802,arXiv: 1709.05543 [hep-ex].

(10)

[7]ATLASCollaboration,IdentiﬁcationofboostedHiggsbosonsdecayinginto

b-quarkpairswith the ATLASdetector at 13TeV, arXiv:1906.11005 [hep-ex], 2019.

[8]M.Cacciari,G.P.Salam,G.Soyez,Theanti-kt jetclusteringalgorithm,J.High

EnergyPhys.04(2008)063,arXiv:0802.1189 [hep-ph].

[9]ATLASCollaboration, Performanceofjet substructure techniquesforlarge-R jetsinproton–protoncollisionsat√s=7 TeV usingtheATLASdetector,J.High EnergyPhys.09(2013)076,arXiv:1306.4945 [hep-ex].

[10]D.Krohn,J.Thaler,L.-T.Wang,Jettrimming,J.HighEnergyPhys.02(2010) 084,arXiv:0912.1342 [hep-ph].

[11]A.J.Larkoski,S.Marzani,G.Soyez,J.Thaler,Softdrop,J.HighEnergyPhys.05 (2014)146,arXiv:1402.2657 [hep-ph].

[12] ATLAS Collaboration, Flavor tagging with track-jets in boosted topologies withtheATLASdetector,ATL-PHYS-PUB-2014-013,https://cds.cern.ch/record/ 1750681,2014.

[13]M.Dasgupta,A.Fregoso,S.Marzani,G.P.Salam,Towardsanunderstandingof jetsubstructure,J.HighEnergyPhys.09(2013)029,arXiv:1307.0007 [hep-ph]. [14]ATLASCollaboration,Identiﬁcationofboosted,hadronicallydecayingW bosons

andcomparisonswith ATLAS datataken at √s=8 TeV,Eur.Phys. J. C76 (2016)154,arXiv:1510.05821 [hep-ex].

[15]S.Marzani,L.Schunk,G.Soyez,Astudyofjetmassdistributionswith groom-ing,J.HighEnergyPhys.07(2017)132,arXiv:1704.02210 [hep-ph]. [16]Z.-B.Kang,K.Lee,X.Liu,F.Ringer,Thegroomedandungroomedjetmass

dis-tributionforinclusivejetproductionattheLHC,J.HighEnergyPhys.10(2018) 137,arXiv:1803.03645 [hep-ph].

[17]C.Frye,A.J.Larkoski,M.D.Schwartz,K.Yan,Precisionphysicswithpile-up in-sensitiveobservables,arXiv:1603.06375 [hep-ph],2016.

[18]C.Frye,A.J.Larkoski,M.D.Schwartz,K.Yan,Factorizationforgroomedjet sub-structurebeyondthenext-to-leadinglogarithm,J.HighEnergyPhys.07(2016) 064,arXiv:1603.09338 [hep-ph].

[19]T.Becher,M.D.Schwartz,Aprecisedeterminationof αsfromLEPthrustdata

usingeffective ﬁeldtheory,J.HighEnergyPhys. 07(2008)034,arXiv:0803. 0342 [hep-ph].

[20]ATLASCollaboration,Measurementofthesoft-dropjetmassinpp collisions

at√s=13 TeV withtheATLASdetector,Phys.Rev.Lett.121(2018)092001, arXiv:1711.08341 [hep-ex].

[21]CMSCollaboration,Measurementsofthedifferentialjetcrosssectionasa func-tionofthejet mass indijeteventsfrom proton-protoncollisionsat √s=

13 TeV,J.HighEnergyPhys.11(2018)113,arXiv:1807.05974 [hep-ex]. [22]CMSCollaboration,Studiesofjetmassindijetand W/Z +jetevents,J.High

EnergyPhys.05(2013)090,arXiv:1303.4811 [hep-ex].

[23] ATLASCollaboration,ATLASinsertableb-layertechnicaldesignreport, ATLAS-TDR-19,https://cds.cern.ch/record/1291633,2010;

ATLAS insertableB-layer technicaldesignreport addendum, ATLAS-TDR-19-ADD-1,https://cds.cern.ch/record/1451888,2012.

[24]B.Abbott,etal.,ProductionandintegrationoftheATLASinsertableb-layer,J. Instrum.13(2018)T05008,arXiv:1803.00844 [physics.ins-det].

[25]ATLASCollaboration,Performanceofthe ATLAStrigger systemin2015,Eur. Phys.J.C77(2017)317,arXiv:1611.09661 [hep-ex].

[26] ATLASCollaboration,Luminositydeterminationinppcollisionsat√s=13 TeV usingtheATLAS detector at theLHC,ATLAS-CONF-2019-021, http://cdsweb. cern.ch/record/2677054,2019.

[27]G.Avoni,et al.,ThenewLUCID-2detectorforluminositymeasurementand monitoringinATLAS,J.Instrum.13(2018)P07017.

[28]ATLASCollaboration, TheATLASsimulationinfrastructure,Eur.Phys. J.C70 (2010)823,arXiv:1005.4568 [physics.ins-det].

[29]S.Agostinelli,etal.,GEANT4:asimulationtoolkit,Nucl.Instrum.MethodsA 506(2003)250.

[30]T.Sjöstrand,S.Mrenna,P.Z.Skands,AbriefintroductiontoPYTHIA8.1,Comput. Phys.Commun.178(2008)852,arXiv:0710.3820 [hep-ph].

[31] ATLASCollaboration,SummaryofATLASPythia8tunes, ATL-PHYS-PUB-2012-003,https://cds.cern.ch/record/1474107,2012.

[32]R.D.Ball,etal.,PartondistributionswithLHCdata,Nucl.Phys.B867(2013) 244–289,arXiv:1207.1303 [hep-ph].

[33]T.Gleisberg,etal.,EventgenerationwithSHERPA1.1,J.HighEnergyPhys.02 (2009)007,arXiv:0811.4622 [hep-ph].

[34]H.-L.Lai,etal.,Newpartondistributionsforcolliderphysics,Phys.Rev.D82 (2010)074024,arXiv:1007.2241 [hep-ph].

[35]J.Alwall,etal.,Theautomatedcomputationoftree-levelandnext-to-leading orderdifferentialcrosssections,andtheirmatchingtopartonshower simula-tions,J.HighEnergyPhys.07(2014)079,arXiv:1405.0301 [hep-ph]. [36] ATLASCollaboration,ATLASPythia8tunesto7 TeV data,

ATL-PHYS-PUB-2014-021,https://cds.cern.ch/record/1966419,2014.

[37]ATLASCollaboration,Measurementsofinclusiveanddifferentialﬁducial cross-sectionsoft¯tγ productioninleptonicﬁnalstatesat√s=13 TeV inATLAS, Eur.Phys.J.C79(2019)382,arXiv:1812.01697 [hep-ex].

[38] ATLASCollaboration,VertexreconstructionperformanceoftheATLASdetector at√s=13 TeV,ATL-PHYS-PUB-2015-026,https://cds.cern.ch/record/2037717, 2015.

[39]ATLASCollaboration,TopologicalcellclusteringintheATLAScalorimetersand itsperformanceinLHCRun1,Eur.Phys.J.C77(2017)490,arXiv:1603.02934 [hep-ex].

[40]M.Cacciari,G.P.Salam,DispellingtheN3_myth_for_the_k

tjet-ﬁnder,Phys.Lett.

B641(2006)57,arXiv:hep-ph/0512210 [hep-ph].

[41]M.Cacciari,G.P.Salam,G.Soyez,FastJetusermanual,Eur.Phys.J.C72(2012) 1896,arXiv:1111.6097 [hep-ph].

[42]S.Catani,Y.L.Dokshitzer,M.H.Seymour,B.R.Webber,Longitudinallyinvariant

k⊥clusteringalgorithmsforhadronhadroncollisions,Nucl.Phys.B406(1993) 187.

[43]S.D.Ellis,D.E.Soper,Successivecombinationjetalgorithmforhadroncollisions, Phys.Rev.D48(1993)3160,arXiv:hep-ph/9305266 [hep-ph].

[44]ATLASCollaboration,Insitucalibrationoflarge-R jetenergyandmassin13 TeVproton-protoncollisionswiththeATLASdetector,Eur.Phys.J.C79(2019) 135,arXiv:1807.09477 [hep-ex].

[45]Y.L.Dokshitzer,G.D.Leder,S.Moretti,B.R.Webber,Betterjetclustering algo-rithms,J.HighEnergyPhys.08(1997)001,arXiv:hep-ph/9707323 [hep-ph]. [46]M.Wobisch,T.Wengler,Hadronizationcorrectionstojetcross-sectionsindeep

inelasticscattering, in:MonteCarlo Generators for HERAPhysics, Proceed-ings,Workshop,Hamburg,Germany,1998–1999,1998,p. 270,arXiv:hep-ph/ 9907280 [hep-ph].

[47] ATLASCollaboration, Impactofalternativeinputsandgroomingmethodson large-R jetreconstructioninATLAS,ATL-PHYS-PUB-2017-020,https://cds.cern. ch/record/2297485,2017.

[48]M. Cacciari,G.P. Salam,G. Soyez, SoftKiller,a particle-levelpileup removal method,Eur.Phys.J.C75(2015)59,arXiv:1407.0408 [hep-ph].

[49]P.Berta,M.Spousta,D.W.Miller, R.Leitner,Particle-levelpileupsubtraction forjetsandjet shapes,J.HighEnergyPhys.06(2014)092,arXiv:1403.3108 [hep-ex].

[50] ATLASCollaboration,Constituent-levelpile-upmitigationtechniquesinATLAS, ATLAS-CONF-2017-065,https://cds.cern.ch/record/2281055,2017.

[51]M.Cacciari,G.P.Salam,Pileupsubtractionusingjetareas, Phys.Lett.B659 (2008)119,arXiv:0707.1378 [hep-ph].

[52]ATLASCollaboration,MuonreconstructionperformanceoftheATLASdetector inproton–protoncollisiondataat√s=13 TeV,Eur.Phys.J.C76(2016)292, arXiv:1603.05598 [hep-ex].

[53]ATLASCollaboration,Optimisationoflarge-radiusjetreconstructionforthe AT-LASdetector in13TeVproton–protoncollisions, arXiv:2009.04986 [hep-ex], 2020.

[54]ATLASCollaboration,Measurementsofb-jettaggingeﬃciencywiththeATLAS detectorusingtt events¯ at√s=13 TeV,J.HighEnergyPhys.08(2018)089, arXiv:1805.01845 [hep-ex].

[55]M.Cacciari,G.P.Salam,G.Soyez,Thecatchmentareaofjets,J.HighEnergy Phys.04(2008)005,arXiv:0802.1188 [hep-ph].

[56]ATLASCollaboration,Performanceofb-jetidentiﬁcationintheATLAS experi-ment,J.Instrum.11(2016)P04008,arXiv:1512.01094 [hep-ex].

[57] ATLASCollaboration,OptimisationoftheATLASb-taggingperformanceforthe 2016 LHC run, ATL-PHYS-PUB-2016-012, https://cds.cern.ch/record/2160731, 2016.

[58] ATLASCollaboration, Calibrationoflight-ﬂavourb-jetmistaggingrates using ATLAS proton–protoncollisiondata at√s=13 TeV,ATLAS-CONF-2018-006, https://cds.cern.ch/record/2314418,2018.

[59] ATLASCollaboration,Measurementofb-taggingeﬃciencyofc-jetsintt events¯

usingalikelihoodapproachwiththeATLASdetector,ATLAS-CONF-2018-001, https://cds.cern.ch/record/2306649,2018.

[60]ATLAS Collaboration, Measurement of the photon identiﬁcation eﬃciencies withtheATLAS detectorusingLHCRun2datacollectedin2015and2016, Eur.Phys.J.C79(2019)205,arXiv:1810.05087 [hep-ex].

[61]ATLASCollaboration,ElectronandphotonenergycalibrationwiththeATLAS detector using 2015–2016LHCproton–proton collision data,J. Instrum. 14 (2019)P03017,arXiv:1812.03848 [hep-ex].

[62]ATLASCollaboration,MonitoringanddataqualityassessmentoftheATLAS liq-uidargoncalorimeter,J.Instrum.9(2014)P07024,arXiv:1405.3768 [hep-ex]. [63] ATLASCollaboration,Selectionofjetsproducedin13 TeV proton–proton

col-lisions with the ATLAS detector, ATLAS-CONF-2015-029, https://cds.cern.ch/ record/2037702,2015.

[64]G.D’Agostini,ImprovediterativeBayesianunfolding,arXiv:1010.0632 [physics. data-an],2010.

[65]T. Adye, Unfolding algorithms and tests using RooUnfold, arXiv:1105.1160 [physics.data-an],2011.

[66]A.Hocker,V.Kartvelishvili, SVDapproachtodata unfolding,Nucl.Instrum. MethodsA372(1996)469,arXiv:hep-ph/9509307 [hep-ph].

[67]ATLASCollaboration,Jetmassandsubstructureofinclusivejetsin√s=7 TeV

pp collisionswiththeATLASexperiment,J.HighEnergyPhys.05(2012)128, arXiv:1203.4606 [hep-ex].

[68]ATLASCollaboration,Measurementoftheinelasticproton–protoncrosssection at√s=13 TeV withtheATLASdetectorattheLHC,Phys.Rev.Lett.117(2016) 182002,arXiv:1606.02625 [hep-ex].

(11)

[69]M.Bahr,etal.,Herwig++physicsandmanual,Eur.Phys.J.C58(2008)639, arXiv:0803.0883 [hep-ph].

[70]J.Bellm,etal.,Herwig7.0/Herwig++3.0releasenote,Eur.Phys.J.C76(2016) 196,arXiv:1512.01178 [hep-ph].

[71]G.Bohm,G.Zech,IntroductiontoStatisticsandDataAnalysisforPhysicists, VerlagDeutschesElektronen-Synchrotron,ISBN 978-3-935702-41-6,2010.

[72] K.Cranmer,G.Lewis,L.Moneta,A.Shibata,W.Verkerke,HistFactory:atoolfor creatingstatisticalmodelsforusewithRooFitandRooStats,tech.rep. CERN-OPEN-2012-016,NewYorkU.,2012,https://cds.cern.ch/record/1456844. [73] ATLAS Collaboration, ATLAS computing acknowledgements,

ATL-SOFT-PUB-2020-001,https://cds.cern.ch/record/2717821.

TheATLASCollaboration

G. Aad102,B. Abbott129,D.C. Abbott103, O. Abdinov13,∗, A. Abed Abud71a,71b, K. Abeling53,

D.K. Abhayasinghe94, S.H. Abidi168, O.S. AbouZeid40,N.L. Abraham157,H. Abramowicz162,H. Abreu161, Y. Abulaiti6, B.S. Acharya67a,67b,q,B. Achkar53, S. Adachi164,L. Adam100,C. Adam Bourdarios65,

L. Adamczyk84a,L. Adamek168,J. Adelman121,M. Adersberger114,A. Adiguzel12c,an, S. Adorni54, T. Adye144, A.A. Affolder146, Y. Aﬁk161,C. Agapopoulou65, M.N. Agaras38,A. Aggarwal119, C. Agheorghiesei27c, J.A. Aguilar-Saavedra140f,140a,am,F. Ahmadov80,W.S. Ahmed104,X. Ai15a, G. Aielli74a,74b,S. Akatsuka86,T.P.A. Åkesson97,E. Akilli54,A.V. Akimov111, K. Al Khoury65, G.L. Alberghi23b,23a,J. Albert177,M.J. Alconada Verzini162,S. Alderweireldt36,M. Aleksa36, I.N. Aleksandrov80,C. Alexa27b, D. Alexandre19,T. Alexopoulos10,A. Alfonsi120,M. Alhroob129, B. Ali142,G. Alimonti69a,J. Alison37,S.P. Alkire149, C. Allaire65,B.M.M. Allbrooke157, B.W. Allen132, P.P. Allport21,A. Aloisio70a,70b, A. Alonso40,F. Alonso89,C. Alpigiani149, A.A. Alshehri57,

M. Alvarez Estevez99, D. Álvarez Piqueras175,M.G. Alviggi70a,70b,Y. Amaral Coutinho81b, A. Ambler104, L. Ambroz135,C. Amelung26,D. Amidei106,S.P. Amor Dos Santos140a,S. Amoroso46,C.S. Amrouche54, F. An79, C. Anastopoulos150, N. Andari145, T. Andeen11, C.F. Anders61b,J.K. Anders20,

A. Andreazza69a,69b,V. Andrei61a, C.R. Anelli177, S. Angelidakis38,A. Angerami39,

A.V. Anisenkov122b,122a,A. Annovi72a,C. Antel61a, M.T. Anthony150, M. Antonelli51, D.J.A. Antrim172, F. Anulli73a, M. Aoki82,J.A. Aparisi Pozo175, L. Aperio Bella36, G. Arabidze107,J.P. Araque140a,

V. Araujo Ferraz81b,R. Araujo Pereira81b,C. Arcangeletti51, A.T.H. Arce49, F.A. Arduh89,J-F. Arguin110, S. Argyropoulos78,J.-H. Arling46,A.J. Armbruster36, L.J. Armitage93, A. Armstrong172,O. Arnaez168, H. Arnold120,A. Artamonov124,∗, G. Artoni135,S. Artz100, S. Asai164,N. Asbah59,

E.M. Asimakopoulou173,L. Asquith157, K. Assamagan29, R. Astalos28a,R.J. Atkin33a, M. Atkinson174, N.B. Atlay152,H. Atmani65, K. Augsten142,G. Avolio36, R. Avramidou60a,M.K. Ayoub15a,

A.M. Azoulay169b,G. Azuelos110,ba,M.J. Baca21, H. Bachacou145,K. Bachas68a,68b, M. Backes135, F. Backman45a,45b, P. Bagnaia73a,73b, M. Bahmani85, H. Bahrasemani153,A.J. Bailey175,V.R. Bailey174, J.T. Baines144, M. Bajic40, C. Bakalis10,O.K. Baker184,P.J. Bakker120, D. Bakshi Gupta8, S. Balaji158, E.M. Baldin122b,122a, P. Balek181,F. Balli145,W.K. Balunas135, J. Balz100,E. Banas85, A. Bandyopadhyay24, Sw. Banerjee182,k,A.A.E. Bannoura183, L. Barak162,W.M. Barbe38, E.L. Barberio105,D. Barberis55b,55a, M. Barbero102, T. Barillari115,M-S. Barisits36,J. Barkeloo132,T. Barklow154,R. Barnea161,S.L. Barnes60c, B.M. Barnett144, R.M. Barnett18,Z. Barnovska-Blenessy60a,A. Baroncelli60a,G. Barone29,A.J. Barr135, L. Barranco Navarro45a,45b,F. Barreiro99,J. Barreiro Guimarães da Costa15a, S. Barsov138,

R. Bartoldus154,G. Bartolini102, A.E. Barton90,P. Bartos28a, A. Basalaev46, A. Bassalat65,au, R.L. Bates57, S.J. Batista168,S. Batlamous35e,J.R. Batley32, B. Batool152, M. Battaglia146,M. Bauce73a,73b,F. Bauer145, K.T. Bauer172,H.S. Bawa31,o,J.B. Beacham49, T. Beau136,P.H. Beauchemin171,F. Becherer52,P. Bechtle24, H.C. Beck53, H.P. Beck20,u, K. Becker52,M. Becker100,C. Becot46, A. Beddall12d, A.J. Beddall12a,

V.A. Bednyakov80, M. Bedognetti120, C.P. Bee156,T.A. Beermann77, M. Begalli81b, M. Begel29, A. Behera156, J.K. Behr46,F. Beisiegel24,A.S. Bell95, G. Bella162,L. Bellagamba23b, A. Bellerive34, P. Bellos9, K. Beloborodov122b,122a, K. Belotskiy112,N.L. Belyaev112, D. Benchekroun35a, N. Benekos10, Y. Benhammou162, D.P. Benjamin6,M. Benoit54, J.R. Bensinger26, S. Bentvelsen120, L. Beresford135, M. Beretta51, D. Berge46, E. Bergeaas Kuutmann173,N. Berger5,B. Bergmann142,L.J. Bergsten26, J. Beringer18, S. Berlendis7, N.R. Bernard103,G. Bernardi136, C. Bernius154,F.U. Bernlochner24, T. Berry94,P. Berta100, C. Bertella15a,I.A. Bertram90, G.J. Besjes40,O. Bessidskaia Bylund183,

N. Besson145,A. Bethani101,S. Bethke115,A. Betti24, A.J. Bevan93, J. Beyer115,R. Bi139, R.M. Bianchi139, O. Biebel114,D. Biedermann19,R. Bielski36, K. Bierwagen100,N.V. Biesuz72a,72b,M. Biglietti75a,

T.R.V. Billoud110,M. Bindi53, A. Bingul12d, C. Bini73a,73b,S. Biondi23b,23a, M. Birman181,T. Bisanz53, J.P. Biswal162,A. Bitadze101, C. Bittrich48,K. Bjørke134, K.M. Black25, T. Blazek28a, I. Bloch46,

C. Blocker26,A. Blue57, U. Blumenschein93,G.J. Bobbink120, V.S. Bobrovnikov122b,122a,S.S. Bocchetta97, A. Bocci49,D. Bogavac14, A.G. Bogdanchikov122b,122a, C. Bohm45a, V. Boisvert94,P. Bokan53, T. Bold84a,

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A.S. Boldyrev113,A.E. Bolz61b,M. Bomben136,M. Bona93, J.S. Bonilla132, M. Boonekamp145,

H.M. Borecka-Bielska91,A. Borisov123, G. Borissov90, J. Bortfeldt36, D. Bortoletto135, V. Bortolotto74a,74b, D. Boscherini23b, M. Bosman14,J.D. Bossio Sola104,K. Bouaouda35a, J. Boudreau139,

E.V. Bouhova-Thacker90, D. Boumediene38,S.K. Boutle57,A. Boveia127, J. Boyd36,D. Boye33c,av, I.R. Boyko80, A.J. Bozson94, J. Bracinik21, N. Brahimi102, G. Brandt183, O. Brandt61a, F. Braren46, B. Brau103, J.E. Brau132,W.D. Breaden Madden57, K. Brendlinger46,L. Brenner46, R. Brenner173, S. Bressler181, B. Brickwedde100,D.L. Briglin21,D. Britton57,D. Britzger115, I. Brock24,R. Brock107, G. Brooijmans39,W.K. Brooks147d,E. Brost121, J.H. Broughton21,P.A. Bruckman de Renstrom85, D. Bruncko28b,A. Bruni23b,G. Bruni23b,L.S. Bruni120, S. Bruno74a,74b,B.H. Brunt32, M. Bruschi23b, N. Bruscino139,P. Bryant37,L. Bryngemark97, T. Buanes17, Q. Buat36, P. Buchholz152,A.G. Buckley57, I.A. Budagov80, M.K. Bugge134,F. Bührer52, O. Bulekov112, T.J. Burch121,S. Burdin91,C.D. Burgard120, A.M. Burger130, B. Burghgrave8, J.T.P. Burr46,J.C. Burzynski103,V. Büscher100, E. Buschmann53,

P.J. Bussey57,J.M. Butler25, C.M. Buttar57, J.M. Butterworth95, P. Butti36,W. Buttinger36,A. Buzatu159, A.R. Buzykaev122b,122a,G. Cabras23b,23a, S. Cabrera Urbán175, D. Caforio56,H. Cai174, V.M.M. Cairo154, O. Cakir4a,N. Calace36,P. Calaﬁura18, A. Calandri102, G. Calderini136,P. Calfayan66,G. Callea57, L.P. Caloba81b, S. Calvente Lopez99, D. Calvet38,S. Calvet38, T.P. Calvet156,M. Calvetti72a,72b, R. Camacho Toro136,S. Camarda36,D. Camarero Munoz99,P. Camarri74a,74b, D. Cameron134, R. Caminal Armadans103,C. Camincher36, S. Campana36, M. Campanelli95,A. Camplani40,

A. Campoverde152, V. Canale70a,70b,A. Canesse104,M. Cano Bret60c, J. Cantero130, T. Cao162, Y. Cao174, M.D.M. Capeans Garrido36, M. Capua41b,41a, R. Cardarelli74a, F. Cardillo150,G. Carducci41b,41a,

I. Carli143,T. Carli36,G. Carlino70a,B.T. Carlson139,L. Carminati69a,69b,R.M.D. Carney45a,45b,

S. Caron119, E. Carquin147d,S. Carrá46,J.W.S. Carter168, M.P. Casado14,f,A.F. Casha168,D.W. Casper172, R. Castelijn120, F.L. Castillo175, V. Castillo Gimenez175,N.F. Castro140a,140e, A. Catinaccio36,

J.R. Catmore134,A. Cattai36, J. Caudron24, V. Cavaliere29,E. Cavallaro14,M. Cavalli-Sforza14,

V. Cavasinni72a,72b,E. Celebi12b, F. Ceradini75a,75b, L. Cerda Alberich175, K. Cerny131, A.S. Cerqueira81a, A. Cerri157,L. Cerrito74a,74b, F. Cerutti18, A. Cervelli23b,23a, S.A. Cetin12b,D. Chakraborty121,

S.K. Chan59,W.S. Chan120,W.Y. Chan91,J.D. Chapman32,B. Chargeishvili160b,D.G. Charlton21, T.P. Charman93,C.C. Chau34,S. Che127, A. Chegwidden107,S. Chekanov6,S.V. Chekulaev169a, G.A. Chelkov80,as,M.A. Chelstowska36,B. Chen79,C. Chen60a, C.H. Chen79,H. Chen29, J. Chen60a, J. Chen39, S. Chen137, S.J. Chen15c,X. Chen15b,az, Y. Chen83, Y-H. Chen46, H.C. Cheng63a,H.J. Cheng15a, A. Cheplakov80,E. Cheremushkina123,R. Cherkaoui El Moursli35e,E. Cheu7, K. Cheung64,

T.J.A. Chevalérias145,L. Chevalier145, V. Chiarella51,G. Chiarelli72a,G. Chiodini68a,A.S. Chisholm36,21_,

A. Chitan27b,I. Chiu164, Y.H. Chiu177,M.V. Chizhov80,K. Choi66,A.R. Chomont73a,73b, S. Chouridou163, Y.S. Chow120, M.C. Chu63a,X. Chu15a,15d, J. Chudoba141, A.J. Chuinard104, J.J. Chwastowski85,

L. Chytka131, K.M. Ciesla85, D. Cinca47,V. Cindro92,I.A. Cioar˘a27b,A. Ciocio18,F. Cirotto70a,70b,

Z.H. Citron181,m,M. Citterio69a,D.A. Ciubotaru27b,B.M. Ciungu168,A. Clark54,M.R. Clark39, P.J. Clark50, C. Clement45a,45b,Y. Coadou102, M. Cobal67a,67c,A. Coccaro55b, J. Cochran79, H. Cohen162,

A.E.C. Coimbra36,L. Colasurdo119,B. Cole39,A.P. Colijn120,J. Collot58,P. Conde Muiño140a,g, E. Coniavitis52,S.H. Connell33c,I.A. Connelly57,S. Constantinescu27b,F. Conventi70a,bb_,

A.M. Cooper-Sarkar135, F. Cormier176, K.J.R. Cormier168, L.D. Corpe95,M. Corradi73a,73b, E.E. Corrigan97, F. Corriveau104,ai,M.J. Costa175, F. Costanza5, D. Costanzo150, G. Cowan94, J.W. Cowley32,J. Crane101, K. Cranmer125,S.J. Crawley57,R.A. Creager137,S. Crépé-Renaudin58, F. Crescioli136,M. Cristinziani24, V. Croft120,G. Crosetti41b,41a,A. Cueto5, T. Cuhadar Donszelmann150, A.R. Cukierman154,

S. Czekierda85, P. Czodrowski36, M.J. Da Cunha Sargedas De Sousa60b, J.V. Da Fonseca Pinto81b, C. Da Via101,W. Dabrowski84a,T. Dado28a, S. Dahbi35e,T. Dai106, C. Dallapiccola103,M. Dam40, G. D’amen23b,23a_,_{V. D’Amico}75a,75b_, _{J. Damp}100_, _{J.R. Dandoy}137_, _{M.F. Daneri}30_, _{N.P. Dang}182,k_, N.S. Dann101,M. Danninger176, V. Dao36,G. Darbo55b,O. Dartsi5,A. Dattagupta132,T. Daubney46, S. D’Auria69a,69b, W. Davey24,C. David46,T. Davidek143, D.R. Davis49, I. Dawson150,K. De8,

R. De Asmundis70a, M. De Beurs120, S. De Castro23b,23a,S. De Cecco73a,73b,N. De Groot119,

P. de Jong120, H. De la Torre107,A. De Maria15c,D. De Pedis73a,A. De Salvo73a, U. De Sanctis74a,74b, M. De Santis74a,74b,A. De Santo157,K. De Vasconcelos Corga102,J.B. De Vivie De Regie65,

C. Debenedetti146, D.V. Dedovich80,A.M. Deiana42, M. Del Gaudio41b,41a, J. Del Peso99,